Digital image editing refers generally to the use of computer software and associated hardware to access a digital image file and perform modifications on the image of the digital image file. In many cases, such modifications may require numerous manual and automated operations, particularly when the image(s) being modified contain a potentially large number of image objects.
Vector images are a type of digital image in which the images, and included image objects, are constructed in terms of geometric shapes. For example, a vector image may include a circle that is defined in terms of its radius. Complex images may be constructed using combinations of various shapes/formulas. Vector images may also be referred to as, or include, vector graphics.
For example, a vector image may include a large number of image objects, such as persons, animals, plants, or structures. For example, a vector image of a field may include a large number of image objects, such as flowers or trees. In many such cases, a user wishing to edit such a vector image may find that a large number of image objects of a particular type (e.g., flower) may be included within the vector image. In other cases, a user may use existing digital image editing tools (e.g., copy/paste functionality) to create duplicates, or near duplicates, of one or more such image objects.
Thus, in various situations, a given vector image may include image objects that are the same as, or very similar to, one another. If a large number of, e.g., flowers, is included in a vector image, the various flowers may be of different sizes, colors, or orientations, or may be placed essentially randomly within the vector image.
Consequently, it may be difficult to identify and locate all image objects of a certain type and a certain level of similarity within a vector image. In fact, even when a vector image includes a relatively small number of similar image objects, it may be a difficult task, whether manual or automated, to locate (and edit) each and every image object of a set of similar image objects.
Nonetheless, many users may wish exactly to perform a particular edit to some or all similar/same image objects within a vector image. For example, a user may wish to change a color, size, or shape of all flowers within a vector image of a field, or all clouds within a vector image of a sky. As just referenced, a user may find it difficult and time-consuming to visually identify each such image object, and, in any case, may find it difficult even to find all such image objects. Further, attempting to automate this task of identifying similar/same vector image objects may require significant computing resources and associated time periods for completing the required processing. Even then, the results of such processing may include either or both of false positives (image objects that are identified as being sufficiently similar, but are not) and false negatives (image objects that are identified as not being sufficiently similar, but are).